Text Clustering using Semantic Terms

In traditional text clustering, documents appear terms frequency without considering the semantic information of each document (i.e., vector model). The property of vector model may be incorrectly classified documents into different clusters when documents of same cluster lack the shared terms. Recently, to overcome this problem uses knowledge based approaches. However, these approaches have an influence of structure of document set and a cost problem of constructing ontology. In this paper, we propose a text clustering method using semantic terms for clustering label and term weights. The semantic terms of clustering label can well express the internal structure of document clusters using non-negative matrix factorization (NMF). It can also improve the quality of text clustering which uses the term weights by WordNet. The experimental results demonstrate that the proposed method achieves better performance than other text clustering methods.